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Age-Related Changes in Vestibular Evoked Myogenic Potentials Using a Modified Blood Pressure Manometer Feedback Method

2010· article· en· W2031138010 on OpenAlexaff
Brandon M. Tourtillott, John A. Ferraro, Ali Bani-Ahmed, Elaine Almquist, Nandini Deshpande

Bibliographic record

VenueAmerican Journal of Audiology · 2010
Typearticle
Languageen
FieldNeuroscience
TopicVestibular and auditory disorders
Canadian institutionsQueen's University
Fundersnot available
KeywordsVestibular evoked myogenic potentialVestibular systemAudiologyMedicineBlood pressureSacculeInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE: To collect age-specific vestibular evoked myogenic potential (VEMP) data and to characterize age-related differences in VEMP parameters using a modified blood pressure manometer (BPM) method of sternocleidomastoid (SCM) muscle monitoring. METHODS: VEMPs were recorded on healthy adults ranging in age from 23 to 84 years with no history of dizziness, neuromuscular pathologies, or cervical complaints. Participants were assigned to 3 groups using a nonprobability static group assignment based on their age. VEMP P1 and N1 latency, threshold, peak-to-peak amplitude, and interamplitude difference (IAD) ratios were obtained at 130 dB SPL. RESULTS: Statistical differences were detected in peak-to-peak mean amplitude and threshold measures among groups. Post hoc analysis revealed that differences shown were between the young group and both older groups. No significant differences were noted in P1 and N1 latencies or IAD ratios. CONCLUSIONS: This study confirmed a significant decline in VEMP amplitude and increase in VEMP thresholds in healthy older persons. Normative age-related data may be necessary to properly interpret VEMP recordings in a clinical setting when evaluating aging populations. The BPM method utilized for controlling SCM muscle may be a valuable alternative to control SCM muscle contraction when electromyography equipment is not available.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.154
Threshold uncertainty score0.866

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.020
GPT teacher head0.288
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations12
Published2010
Admission routes1
Has abstractyes

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